Image-Based Probe Positioning

ABSTRACT

A framework for image-based probe positioning is disclosed herein. The framework receives a current image from a probe. The current image is acquired by the probe within a structure of interest. The framework predicts a position of the probe and generates a recommendation of a next maneuver to be performed using the probe by applying the current image to a trained classifier. The framework then outputs the predicted position and the recommendation of the next maneuver.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of prior application Ser. No.16/372,727, filed Apr. 2, 2019, which is hereby incorporated byreference in entirety.

TECHNICAL FIELD

The present disclosure generally relates to facilitating image-basedpositioning of a probe.

BACKGROUND

Echocardiography uses ultrasound waves to acquire images of structuresinside the heart. Intracardiac echocardiogram (ICE) is a catheter-basedform of echocardiography that acquires images from within the heart,rather than by gathering images of the heart by sending sound wavesthrough the chest wall. For example, the ACUSON AcuNav™ ultrasoundimaging catheter from Siemens Healthineers Global is used for ICE. Anechocardiogram works by sending medically safe sound waves from atransducer. As the sound waves reflect back from structures in the heartto the transducer, the echocardiogram machine receives the reflectedsound waves and creates a moving picture of the heart's internalstructures. The echo transducer is typically located at the tip of acatheter, which is a thin, flexible tube that is inserted through apuncture into the blood vessel to the heart.

ICE users may need navigation assistance while manipulating the catheterinside the heart. ICE catheter guidance within the heart is typicallyestablished using electro-magnetic based position sensors. A third partysystem (e.g., CARTO® mapping system) receives and interprets the datafrom these sensors to determine positions. Where sensors are notavailable, catheter position is often determined by a secondsupplemental imaging modality such as fluoroscopy. Alternatively,step-by-step guidance may be manually provided by a clinical ultrasoundspecialist.

However, there are various disadvantages in using such systems. Forexample, sensor-based guidance systems are typically more expensive andcumbersome as they typically involve a longer inflexible distal tip ofthe catheter, which thereby reduces maneuverability and increaseschances of heart wall perforation. Additionally, the sensors maypotentially interfere with nearby biomedical devices and instruments.Further, there is a higher risk of catheter manufacturing failure, alongwith lower yield, as well as higher material and labor cost.Fluoroscopy-based navigation systems expose physicians, patients orhospital staff to additional X-ray radiation, which may result inundesirable side effects. As for manual guidance, it requires thepresence of a trained ICE sonographer, and involves additional costsassociated with logistics, scheduling and procedure.

SUMMARY

Described herein is a framework for image-based probe positioning. Theframework receives a current image from a probe. The current image isacquired by the probe within a structure of interest. The frameworkpredicts a position of the probe and generates a recommendation of anext maneuver to be performed using the probe by applying the currentimage to a trained classifier. The framework then outputs the predictedposition and the recommendation of the next maneuver.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete appreciation of the present disclosure and many of theattendant aspects thereof will be readily obtained as the same becomesbetter understood by reference to the following detailed descriptionwhen considered in connection with the accompanying drawings.

FIG. 1 is a block diagram illustrating an exemplary system;

FIG. 2 shows an exemplary probe positioning method;

FIG. 3 shows an exemplary intracardiac echocardiogram (ICE) image;

FIG. 4 illustrates exemplary input and output of the trained neuralnetwork; and

FIG. 5 shows an exemplary user interface.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forthsuch as examples of specific components, devices, methods, etc., inorder to provide a thorough understanding of implementations of thepresent framework. It will be apparent, however, to one skilled in theart that these specific details need not be employed to practiceimplementations of the present framework. In other instances, well-knownmaterials or methods have not been described in detail in order to avoidunnecessarily obscuring implementations of the present framework. Whilethe present framework is susceptible to various modifications andalternative forms, specific embodiments thereof are shown by way ofexample in the drawings and will herein be described in detail. Itshould be understood, however, that there is no intent to limit theinvention to the particular forms disclosed, but on the contrary, theintention is to cover all modifications, equivalents, and alternativesfalling within the spirit and scope of the invention. Furthermore, forease of understanding, certain method steps are delineated as separatesteps; however, these separately delineated steps should not beconstrued as necessarily order dependent in their performance.

The term “x-ray image” as used herein may mean a visible x-ray image(e.g., displayed on a video screen) or a digital representation of anx-ray image (e.g., a file corresponding to the pixel output of an x-raydetector). The term “in-treatment x-ray image” as used herein may referto images captured at any point in time during a treatment deliveryphase of an interventional or therapeutic procedure, which may includetimes when the radiation source is either on or off. From time to time,for convenience of description, CT imaging data (e.g., cone-beam CTimaging data) may be used herein as an exemplary imaging modality. Itwill be appreciated, however, that data from any type of imagingmodality including but not limited to x-ray radiographs, MRI, PET(positron emission tomography), PET-CT, SPECT, SPECT-CT, MR-PET, 3Dultrasound images or the like may also be used in variousimplementations.

Unless stated otherwise as apparent from the following discussion, itwill be appreciated that terms such as “segmenting,” “generating,”“registering,” “determining,” “aligning,” “positioning,” “processing,”“computing,” “selecting,” “estimating,” “detecting,” “tracking,” or thelike may refer to the actions and processes of a computer system, orsimilar electronic computing device, that manipulates and transformsdata represented as physical (e.g., electronic) quantities within thecomputer system's registers and memories into other data similarlyrepresented as physical quantities within the computer system memoriesor registers or other such information storage, transmission or displaydevices. Embodiments of the methods described herein may be implementedusing computer software. If written in a programming language conformingto a recognized standard, sequences of instructions designed toimplement the methods can be compiled for execution on a variety ofhardware platforms and for interface to a variety of operating systems.In addition, implementations of the present framework are not describedwith reference to any particular programming language. It will beappreciated that a variety of programming languages may be used.

As used herein, the term “image” refers to multi-dimensional datacomposed of discrete image elements (e.g., pixels for 2D images, voxelsfor 3D images, doxels for 4D datasets). The image may be, for example, amedical image of a subject collected by computer tomography, magneticresonance imaging, ultrasound, or any other medical imaging system knownto one of skill in the art. The image may also be provided fromnon-medical contexts, such as, for example, remote sensing systems,electron microscopy, etc. Although an image can be thought of as afunction from R³ to R², or a mapping to R³, the present methods are notlimited to such images, and can be applied to images of any dimension,e.g., a 2D picture, 3D volume or 4D dataset. For a 2- or 3-Dimensionalimage, the domain of the image is typically a 2- or 3-Dimensionalrectangular array, wherein each pixel or voxel can be addressed withreference to a set of 2 or 3 mutually orthogonal axes. The terms“digital” and “digitized” as used herein will refer to images orvolumes, as appropriate, in a digital or digitized format acquired via adigital acquisition system or via conversion from an analog image.

The terms “pixels” for picture elements, conventionally used withrespect to 2D imaging and image display, “voxels” for volume imageelements, often used with respect to 3D imaging, and “doxels” for 4Ddatasets can be used interchangeably. It should be noted that the 3Dvolume image is itself synthesized from image data obtained as pixels ona 2D sensor array and displays as a 2D image from some angle of view.Thus, 2D image processing and image analysis techniques can be appliedto the 3D volume image data. In the description that follows, techniquesdescribed as operating upon doxels may alternately be described asoperating upon the 3D voxel data that is stored and represented in theform of 2D pixel data for display. In the same way, techniques thatoperate upon voxel data can also be described as operating upon pixels.In the following description, the variable x is used to indicate asubject image element at a particular spatial location or, alternatelyconsidered, a subject pixel. The terms “subject pixel”, “subject voxel”and “subject doxel” are used to indicate a particular image element asit is operated upon using techniques described herein.

One aspect of the present framework utilizes images input to amachine-learned classifier to predict the position (e.g., location andorientation) of a probe inside a structure of interest, such as withinthe heart of a patient. The predicted position may be used with atreatment or navigation protocol to generate guidance in real time for auser to, for example, steer (e.g., torque, rotate and/or translate) theprobe to a desired position to visualize anatomy.

The framework relies on images acquired by the probe, and avoids the useof sensors and additional secondary imaging modalities. Positioning (ornavigational) guidance generated by the present image-based frameworkprovides several advantages. For example, ICE users can be less relianton fluoroscopy, other on-site experts or position sensor feedback. ICEimaging is a specialized task, and many physicians are previouslyreluctant to use it because it is very difficult for them to preciselyknow the location of the catheter. Even experienced doctors can strugglewith ICE positioning. They often rely on ICE sonographers or need tospend longer time navigating to standard ICE views for reorientationduring a procedure. The present framework not only enables ICE users tobe better informed about their device position, it may also providestep-by-step guidance to complete the procedure. Such framework canincrease user confidence, facilitate user training, reduce fluoroscopyuse and expand ICE adoption. In addition, by removing the use ofposition sensors, costs associated with using the one-time use cathetersare advantageously reduced. The present framework requires no additionalhardware on the ICE catheter, does not increase cost or reducemaneuverability. It also advantageously streamlines workflows, increasesconfidence in ICE usage, and in doing so increases ICE adoption.

It is understood that while a particular application directed tonavigating an ICE catheter may be shown herein, the technology is notlimited to the specific implementations illustrated. The technology mayalso be applied to guiding other types of probes (e.g., needle, stent,endoscope, angioplasty balloon, etc.) internal to an object or structureof interest, such as within the body of a patient.

FIG. 1 is a block diagram illustrating an exemplary system 100. Thesystem 100 includes a computer system 101 for implementing the frameworkas described herein. Computer system 101 may be a desktop personalcomputer, a portable laptop computer, another portable device, amini-computer, a mainframe computer, a server, a cloud infrastructure, astorage system, a dedicated digital appliance, a communication device,or another device having a storage sub-system configured to store acollection of digital data items. In some implementations, computersystem 101 operates as a standalone device. In other implementations,computer system 101 may be connected (e.g., using a network) to othermachines, such as imaging device 102 and workstation 103. In a networkeddeployment, computer system 101 may operate in the capacity of a server(e.g., thin-client server, such as Syngo®.via by Siemens Healthcare), aclient user machine in server-client user network environment, or as apeer machine in a peer-to-peer (or distributed) network environment.

Computer system 101 may include a processor device or central processingunit (CPU) 104 coupled to one or more non-transitory computer-readablemedia 105 (e.g., computer storage or memory), a display device 108(e.g., monitor) and various input devices 110 (e.g., mouse or keyboard)via an input-output interface 121. Computer system 101 may furtherinclude support circuits such as a cache, a power supply, clock circuitsand a communications bus. Various other peripheral devices, such asadditional data storage devices and printing devices, may also beconnected to the computer system 101.

The present technology may be implemented in various forms of hardware,software, firmware, special purpose processors, or a combinationthereof, either as part of the microinstruction code or as part of anapplication program or software product, or a combination thereof, whichis executed via the operating system. In one implementation, thetechniques described herein are implemented as computer-readable programcode tangibly embodied in one or more non-transitory computer-readablemedia 105. In particular, the present techniques may be implemented by amachine learning unit 106 and a processing unit 107. Non-transitorycomputer-readable media 105 may further include random access memory(RAM), read-only memory (ROM), magnetic floppy disk, flash memory, andother types of memories, or a combination thereof. The computer-readableprogram code is executed by processor device 104 to process images orimage data acquired by, for example, imaging device 102. As such, thecomputer system 101 is a general-purpose computer system that becomes aspecific purpose computer system when executing the computer-readableprogram code. The computer-readable program code is not intended to belimited to any particular programming language and implementationthereof. It will be appreciated that a variety of programming languagesand coding thereof may be used to implement the teachings of thedisclosure contained herein.

The same or different computer-readable media 105 may be used forstoring image datasets, patient records, knowledge base, and so forth.Such data may also be stored in external storage or other memories. Theexternal storage may be implemented using a database management system(DBMS) managed by the processor device 104 and residing on a memory,such as a hard disk, RAM, or removable media. The external storage maybe implemented on one or more additional computer systems. For example,the external storage may include a data warehouse system residing on aseparate computer system, a picture archiving and communication system(PACS), or any other now known or later developed hospital, medicalinstitution, medical office, testing facility, pharmacy or other medicalpatient record storage system.

The probe 111 is a steerable device that is inserted into a structure ofinterest in an object, such as the body of a patient. For example, theprobe 111 is positioned in an orifice of the patient, such as throughthe mouth and into the esophagus. Alternatively, the probe 111 ispositioned by surgical insertion through the skin of the patient, suchas for minimally invasive surgery. In other implementations, the probe111 is inserted in an opening created as part of a surgery, such as aninter-operative probe.

The probe 111 may be an intra-operative probe, inter-cavity probe,catheter, or other medical device. In some implementations, the probe111 is any catheter for intervention or other use within a patient. Thecatheter may be sized and shaped for use in the circulatory system, suchas having a diameter of 10 French or less, and a length of a foot ormore. Alternatively, the catheter may be sized and shaped for use atother locations in the body. The catheter is adapted for insertionwithin the patient, such as through a vessel or vein for extending intoa heart chamber, body cavity, or other location within the patient. Thecatheter may include guide wires or be inserted through anotherpreviously positioned guide catheter. The catheter may include anelectrode, scalpel, balloon, stent, imaging array, tube for injection,or other device for treatment of the patient.

In some implementations, the probe 111 includes an imaging source 112.The imaging source 112 is an array, sensor, lens, transducer, or otherelement for imaging or scanning the patient from the probe 111. Forexample, the imaging source 112 in the catheter is an ultrasoundtransducer element or array of an intracardiac echocardiography (ICE)catheter, an ultrasound transducer element of an intravascularultrasound (IVUS) catheter, a lens or camera of an optical coherencetomography (OCT) catheter, a lens or camera of an optical imagingcatheter, or is an ultrasound transducer array of a transesophagealechocardiogram (TEE) ultrasound transducer.

The imaging device 102 is external to or within the probe 111. Forexample, the imaging device 102 is an ultrasound system with abeamformer, detector, and/or image processor connected to the imagingsource 112 but positioned externally to the patient. The externalultrasound system connects with the imaging source 112 to scan. Asanother example, the imaging device 102 is a camera or video device foroptical imaging. The camera or video connects with the imaging source112 to view the patient from the probe 111. In yet another example, theimaging device 102 is an optical coherence imager. In another example,the imaging device 102 is a magnetic resonance (MR) system. The MRsystem connects with a local coil as the imaging source 112 in the probe111. The imaging device 102 uses the imaging source 112 to view or scanthe patient from the probe 111. Alternatively, the imaging device 102 isany modality for scanning a patient from an internal or externallocation, such as a magnetic resonance, computed tomography, positronemission tomography, or single photon emission tomography system.

As an ultrasound transducer element or array, the imaging source 112 maybe used for scanning a one, two, or three-dimensional region of apatient from the probe 111. A piezoelectric or microelectromechanical(e.g., capacitive membrane ultrasound transducer) element or elementstransduce between electrical and acoustic energies for scanning thepatient. An array of such elements may be used to electronically scan orsteer in two or three dimensions. A single element or an array ofelements may be used to mechanically scan in one or two dimensions. Forexample, an element or elements connect with a drive shaft and arerotated within the probe 111. The rotation causes scanning withultrasound of different locations around the probe 111. Otherarrangements may be provided.

The workstation 103 may include a computer and appropriate peripherals,such as a keyboard and display device, and can be operated inconjunction with the entire system 100. For example, the workstation 103may communicate with the imaging device 102 so that the image datacollected by the imaging device 102 can be rendered at the workstation103 and viewed on a display device.

The workstation 103 may communicate directly with the computer system101 to display processed image data and/or output image processingresults via a graphical user interface. Alternatively, the computersystem 101 itself may display processed image data and/or output imageprocessing results via a graphical user interface on display device 108without workstation 103. The workstation 103 may include a graphicaluser interface to receive user input via an input device (e.g.,keyboard, mouse, touch screen, voice or video recognition interface,etc.) to manipulate visualization and/or processing of the image data.For example, the user may view the processed image data, and specify oneor more view adjustments or preferences (e.g., zooming, cropping,panning, rotating, changing contrast, changing color, changing viewangle, changing view depth, changing rendering or reconstructiontechnique, etc.).

It is to be further understood that, because some of the constituentsystem components and method steps depicted in the accompanying figurescan be implemented in software, the actual connections between thesystem components (or the process steps) may differ depending upon themanner in which the present framework is programmed. Given the teachingsprovided herein, one of ordinary skill in the related art will be ableto contemplate these and similar implementations or configurations ofthe present framework.

FIG. 2 shows an exemplary probe positioning method 200 performed by acomputer system. It should be understood that the steps of the method200 may be performed in the order shown or a different order.Additional, different, or fewer steps may also be provided. Further, themethod 200 may be implemented with the system 100 of FIG. 1 , adifferent system, or a combination thereof.

At 202, machine learning unit 106 receives training images of astructure of interest. A mapping between the training images and therespective probe positions from which the training images were acquiredmay also be received. The training images are acquired by a probe atparticular positions within a structure of interest from differentpatients. The training images may be, for example, ICE images, which areultrasound images of adjacent tissues acquired by the distal tip ofsteerable ICE catheters. The structure of interest is any anatomicstructure identified for study. The structure of interest may be, forexample, part or all of a heart or cardiac system (e.g., valve, vessel,artery, heart chamber). The training images may alternatively oradditionally represent all or parts of organs, bone or other structureof interest in the patient.

Each training image represents locations distributed on atwo-dimensional (2D) view or plane. Each view may be mapped to arelative position (e.g., location and orientation) of the probe. A viewis a visualization of a predefined set of one or more anatomiclandmarks. For example, the Home View may be mapped to a Home Positionof the ICE catheter. The Home View may be predefined as a visualizationof the right atrium (RA), right ventricle (RV) and tricuspid valve (TV)of the heart. FIG. 3 shows an exemplary ICE image 301 that representsthe Home View. The Home View was acquired when the ICE catheter waspositioned in the middle of the RA in an unlocked or neutral position(i.e., “Home Position”), meaning there is no steering and the tensionlock is disengaged. This Home view can be used as a “basic point ofnavigation” from which other views can be derived.

The position of the probe may be defined relative to this Home Position,which is considered as the starting position in a typical navigationprotocol. This method of navigation is similar to a human navigating acar based on street signs and landmarks instead of using a globalpositioning system (GPS). By rotating the ICE catheter in a clockwisedirection relative to this Home position, the aortic valve, leftventricle and right ventricular outflow tracts are in the field of viewbefore the mitral valve and left atrium (LA) with the left atrialappendage appear in the image view. With more clockwise rotation of theICE catheter, the left superior pulmonary vein (LSPV) and left inferiorpulmonary vein (LIPV) are visualized in the image view. When the ICEcatheter is in a further posterior direction, the esophagus, descendingaorta and right pulmonary veins (PVs) appear in the image view. Themapping between the image view and the relative position of the probefrom which the image was acquired may be provided by, for example, anexpert sonographer who has observed a large number of images anddetermined the most likely position of the probe. This expertuser-derived mapping based on the multitude of images may then beutilized to train a machine learning classifier, as will be describedlater.

Returning to FIG. 2 , at 204, machine learning unit 106 uses thetraining images to train a classifier to predict a relative position(e.g., location within a cardiac structure, imaging plane orientation,catheter flexion) of the probe based on an input image. The mappingbetween the image view and the relative position of the probe is used asground truth for the training. In some implementations, the classifieris further trained to provide a recommendation of the next one or moremaneuvers to steer the probe to the next location as required by anavigation protocol. The one or more maneuvers may be represented bynavigational instructions. The navigational instructions may bedisplayed to guide a human user to steer (e.g., advance, pull back,rotate or flex) the probe to a particular position as required by anavigation protocol. Alternatively, the navigational instructions may bein the form of machine instructions that are executable by a roboticcontroller to automatically steer the probe to a desired position.

In some implementations, the classifier is further trained to correctany error in the predicted position of the probe by using a time historyof position predictions. For example, consider a situation wherein thetrained classifier first predicts the Home view and then predicts thenext view to be a transseptal view of the left atrium (LA). Thissequence of position predictions to capture views in such order is notpossible. The classifier may further be trained to catch such anexception and determine the next most likely position (or view) giventhe time history of position predictions.

The classifier may be any one or more classifiers. A single class orbinary classifier, collection of different classifiers, cascadedclassifiers, hierarchal classifier, multi-class classifier, model-basedclassifier, classifier based on machine learning or combinations thereofmay be used. Multi-class classifiers include CART, K-nearest neighbors,neural network (e.g., multi-layer perceptron), mixture models or others.In some implementation, the classifier is a neural network. The neuralnetwork may be, for example, a five-layer convolutional neural network(CNN). The weights of the classifier are adjusted as the trainingproceeds until the classifier performs adequately.

At 206, processing unit 107 receives a current image from the probe 111.The current image is acquired by the probe 111 when it is inserted intothe structure of interest (e.g., cardiac system). To acquire the currentimage, the probe 111 may be guided and positioned in the structure ofinterest using steering wires and/or a previously positioned guide. Thecurrent image is the same type and for the same or similar structure ofinterest as the training images (e.g., ICE images of the heart).

At 208, processing unit 107 applies the current image to the trainedclassifier to predict a position of the probe 111 and generate arecommendation of the next one or more maneuvers to steer the probe tothe next location as required by the navigation protocol. In someimplementations, the robustness of the position predictions is increasedby utilizing a time history of position predictions in addition to thecurrent image to predict the position of the probe 111. The time historyof position predictions is a current sequence of a predetermined numberof positions that have been previously predicted by the trainedclassifier. The time history of position predictions may be used by thetrained classifier to detect and correct any error (or exception) in thepredicted position.

At 210, processing unit 107 outputs the predicted position andrecommendation of the next one or more maneuvers. In someimplementations, processing unit 107 displays the probe 111 at thepredicted position in a graphical representation of the structure ofinterest. The graphical representation may be displayed via a graphicaluser interface at, for example, workstation 103. The graphicalrepresentation may be, for example, a catheter tip overlaid on a planeprojection of a three-dimensional rendering of the structure of interestor an image-derived model of the structure of interest. The graphicalrepresentation provides a visual guide of where the probe 111 ispredicted to be currently located.

In some implementations, the predicted one or more maneuvers arerepresented by navigational instructions to guide a human user insteering (e.g., advancing, pulling back, rotating or flexing) the probe111 to a specific location as part of a selected treatment ornavigational protocol. The navigation instructions may be displayed in abox next to (e.g., below or above) the graphical representation of thepredicted position. Alternatively, the navigational instructions may bein the form of machine instructions that are executable by a roboticcontroller (or processor) to automatically steer the probe 111 to thedesired position.

Accordingly, the probe 111 may be repositioned to a new position. Adifferent current image may be acquired by the probe 111 at the newposition. Steps 206, 208 and 210 may be repeated to update the graphicalrepresentation in substantially real time as the probe is navigatedwithin the structure of interest and acquires new current images.

FIG. 4 illustrates exemplary input and output of the trained neuralnetwork 404. The neural network 404 may be trained using theaforementioned method 200. When the trained neural network 404 receivesa new ICE image 402 a-c as input, it predicts the most likely positionof the catheter 406 a-c. The catheter 406 a-c may be displayed at thepredicted position within a graphical representation 408 a-c of theheart on the imaging screen. The graphical representation 408 a-c may bea generic 3D rendering of the heart or a computed tomography (CT)image-derived heart model of the patient. The graphical representation408 a-c may be updated in real time based on the input ICE image 402a-c, as the user navigates the catheter 406 a-c within the heart.

FIG. 5 shows an exemplary user interface 501. The exemplary userinterface 501 may be displayed at, for example, workstation 103 to guidethe user in navigating an ICE catheter in the patient's heart. The userinterface 501 shows a current ultrasound 2D image 502 acquired by theICE catheter at its current position. Based on the current image 502,the classifier trained by the machine learning unit 106 predicts themost likely current position of the catheter. The catheter is displayedat the predicted position in a graphical representation 504 of theheart. The graphical representation 504 may be displayed next to thecurrent image.

In some implementation, a box 506 is positioned below (or next) to thecurrent image 502. The box 506 may display navigational instructions ofthe predicted next maneuver. The navigational instructions may berepresented by, for example, text, diagram, cartoon, arrows indicatingaction(s) or maneuver(s) to perform, or a combination thereof. Thenavigational instructions may guide the user to steer and/or rotate thecatheter to the next position to obtain the next view according to anavigational protocol. The graphical representation 504 and thenavigational instructions in the box 506 may be updated in real time asthe catheter moves and acquires new current ultrasound images 502.

While the present framework has been described in detail with referenceto exemplary embodiments, those skilled in the art will appreciate thatvarious modifications and substitutions can be made thereto withoutdeparting from the spirit and scope of the invention as set forth in theappended claims. For example, elements and/or features of differentexemplary embodiments may be combined with each other and/or substitutedfor each other within the scope of this disclosure and appended claims.

What is claimed is:
 1. One or more non-transitory computer-readablemedia embodying instructions executable by machine to perform operationsfor intracardiac catheter positioning, comprising: (i) receiving acurrent intracardiac echocardiogram (ICE) image acquired by a catheterconfigured to be inserted into a heart; (ii) predicting a position ofthe catheter and generating a recommendation of a next maneuver to beperformed using the catheter by applying the current ICE image and atime history of a plurality of views to a trained classifier, whereinthe time history of the plurality of views comprises a current sequenceof a predetermined number of the views previously generated by thetrained classifier; and (iii) displaying the predicted position and therecommendation of the next maneuver.
 2. The one or more non-transitorycomputer-readable media of claim 1 wherein the displaying the predictedposition comprises displaying the catheter at the predicted position ina graphical representation of the heart.
 3. The one or morenon-transitory computer-readable media of claim 1 wherein the operationsfurther comprise training the classifier based on a plurality of ICEtraining images and using a mapping between the plurality of ICEtraining images and a plurality of ICE catheter positions from which theplurality of ICE training images were acquired as ground truth.
 4. Theone or more non-transitory computer-readable media of claim 1 whereinthe next maneuver is represented by navigational instructions.
 5. Theone or more non-transitory computer-readable media of claim 4 whereinthe navigational instructions comprise machine instructions that areexecutable by a robotic controller to automatically steer the catheterto a desired position.
 6. A method of probe positioning, comprising: (i)receiving a current image acquired by a probe within a structure ofinterest; (ii) predicting a position of the probe and generating arecommendation of a next maneuver to be performed using the probe byapplying the current image and a time history of a plurality of views toa trained classifier, wherein the time history of the plurality of viewscomprises a current sequence of a predetermined number of the viewspreviously generated by the trained classifier; and (iii) displaying thepredicted position and the recommendation of the next maneuver.
 7. Themethod of claim 6 wherein the current image comprises an intracardiacechocardiogram (ICE) image.
 8. The method of claim 6 further comprisingtraining the classifier based on a plurality of training images andusing a mapping between the plurality of training images and a pluralityof probe positions from which the plurality of training images wereacquired as ground truth.
 9. The method of claim 6 wherein the nextmaneuver is represented by navigational instructions.
 10. The method ofclaim 9 wherein the navigational instructions comprise machineinstructions that are executable by a robotic controller toautomatically steer the probe to a desired position.
 11. The method ofclaim 6 wherein the displaying the predicted position comprisesdisplaying the probe at the predicted position in a graphicalrepresentation of the structure of interest.
 12. The method of claim 11wherein the graphical representation of the structure of interestcomprises a three-dimensional rendering of the structure of interest.13. The method of claim 11 wherein the graphical representation of thestructure of interest comprises an image-derived model of the structureof interest.
 14. The method of claim 11 further comprising repeatingsteps (i), (ii) and (iii) to update the graphical representation insubstantially real time as the probe acquires a new current image at anew position.
 15. The method of claim 6 wherein the time history of theplurality of views comprises a home view, an aortic valve view, a leftventricle view, a right ventricular outflow tract view, a left atriumview, a left superior pulmonary vein view, a left inferior pulmonaryvein view, an esophagus view, a right pulmonary veins view, or acombination thereof.
 16. A system for probe positioning, comprising: anon-transitory memory device for storing computer readable program code;and a processor in communication with the non-transitory memory device,the processor being operative with the computer readable program code toperform steps including (i) receiving a current image acquired by aprobe within a structure of interest, (ii) predicting a position of theprobe and generating a recommendation of a next maneuver to be performedusing the probe by applying the current image and a time history of aplurality of views to a trained classifier, wherein the time history ofthe plurality of views comprises a current sequence of a predeterminednumber of the views previously generated by the trained classifier, and(iii) displaying the predicted position and the recommendation of thenext maneuver.
 17. The system of claim 16 wherein the probe comprises anintracardiac echocardiogram (ICE) catheter.
 18. The system of claim 16wherein the processor is operative with the computer readable programcode to display the predicted position by displaying the probe at thepredicted position in a graphical representation of the structure ofinterest.
 19. The system of claim 18 wherein the graphicalrepresentation comprises a plane projection of a three-dimensionalrendering of the structure of interest.
 20. The system of claim 16wherein the time history of the plurality of views comprises a homeview, an aortic valve view, a left ventricle view, a right ventricularoutflow tract view, a left atrium view, a left superior pulmonary veinview, a left inferior pulmonary vein view, an esophagus view, a rightpulmonary veins view, or a combination thereof.